First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact

Here we retrieve global daily 1 km gapless PM2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth’s populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities. Wave-like dramatic changes in air quality are clearly seen around the world before, during, and after the COVID-19 lockdowns, as is the mortality burden linked to fluctuating air pollution events. Encouragingly, only approximately one-third of all countries return to pre-pandemic pollution levels. Many nature-induced air pollution episodes are also revealed, such as biomass burning.

Supplementary Figure 6.PM 2.5 pollution during a severe haze event in South Asia.Spatial distributions of our model-derived (background shading) and ground-measured (dots) daily PM2.5 concentrations (unit: μg m -3 ) during a severe haze event that occurred from 27 October to 18 November 2020 in South Asia.Supplementary Figure 7. Daily PM 2.5 driving factor analysis with XAI.Driving factor analysis of daily PM2.5 pollution (a) on a global scale and within specific localized custom regions: (b) East Asia, (c) South Asia, (d) Africa, (e) Europe, (f) Western North America, (g) Eastern North America, and (h) South America, using Explainable Machine Learning (XAI), with sorted permutation importance scores for each feature.Refer to Supplementary Table 4 for abbreviation definitions.

Supplementary Figure 1 .
Independent spatiotemporal cross-validation results.Density scatterplots of spatial (a) out-of-station, (b) out-of-grid, (c) out-of-state, and temporal (d) out-ofday, (e) out-of-week, and (f) out-of-month cross-validation results of daily PM2.5 predictions (unit: μg m -3 ) against ground-based measurements (unit: μg m -3 ) at all monitoring stations from 2017 to 2022 over land (number of samples = 7,089,428).Black dashed lines represent 1:1 lines, and red solid lines represent best-fit lines from linear regression.The linear relation, coefficient of determination (R 2 ), root-mean-square error (RMSE), normalized RMSE (NRMSE), and mean absolute error (MAE) are given in each panel.Supplementary Figure 2. Example of a daily PM 2.5 global map without satellite AOD gap filling.Spatial distribution of global PM2.5 concentrations (unit: μg m -3 ) without AOD gap filling on 8 October 2020.Zoomed-in regions show PM2.5 concentrations (unit: μg m -3 ) measured at monitoring sites (colored dots) over the (a) western United States, (b) central South America, (c) eastern United States, (d) Europe, (e) northwestern Africa, (f) Australia, (g) India, and (h) eastern China.Thin black lines represent country boundaries or shorelines, and gray lines represent state or provincial boundaries.The maps were created using ESRI ArcGIS Pro 3.0.1.Supplementary Figure 5. PM 2.5 pollution during a severe haze event in eastern China.Spatial distributions of our model-derived (background shading) and ground-measured (dots) daily PM2.5 concentrations (unit: μg m -3 ) for a typical example of a severe haze event that occurred from 31 December 2019 to 7 January 2020 in eastern China.
-Niño years.Regional relative differences (unit: %) in PM2.5 concentrations between the El Niño year (2020) during a month with the most wildfire records and normal years (2018-2019) during the same months for (a) North America in September, (b) South America in October, and (c) Australia in January.Monthly fire emissions (unit: Gg) in 2020 from the Fire Energetics and Emissions Research (FEER) database in these regions and months are shown in (d-f).Blue boundaries represent the defined regions of the western United States, central South America, and southeastern Australia.The maps were created using ESRI ArcGIS Pro 3.0.1.Supplementary Figure 9. Short-term impact of the COVID-19 lockdown on air quality.Time series of 7-day moving average daily population-weighted PM2.5 concentrations (unit: μg m -3 ), and relative differences (unit: %) in PM2.5 concentrations during the same strictest lockdown period comparing the pre-pandemic (2018-2019) and post-pandemic (2021-2022) eras to the pandemic year (2020) in (a-c) China and (d-f) India.The time interval between gray dashed lines in (a) and (d) indicates the most stringent lockdown period, determined by the Oxford Coronavirus Government Response Tracker (OxCGRT) stringency index (indicated by the black solid lines in a & d).Supplementary Figure 10.Comparing PM 2.5 pollution levels and exposure risks at 1 and 10 km resolutions.Spatial comparisons of PM2.5 pollution level (unit: μg m -3 ) and exposure risk (unit: %) at 1 km and 10 km spatial resolutions in major cities: (a) New York, United States; (b) Sao Paulo, Brazil; (c) Johannesburg, South Africa; (d) Rome, Italy; (e) Tehran, Iran; (f) Seoul, South Korea; (g) Jakarta, Indonesia, and (h) Guangzhou, China.Solid brown lines represent roads.Supplementary Figure 13.Spatial coverage of daily MAIAC AOD retrievals.Spatial distribution of coverage (unit: %) of daily MAIAC AOD retrievals over land, where the insert plot shows the daily times series of spatial coverage.The map was created using ESRI ArcGIS Pro 3.0.1.Supplementary Figure 14.Before and after satellite AOD gap filling.Comparison of spatial patterns of daily AOD (a) before and (b) after gap filling over land on an individual day.Areas outlined in red show how gap filling reveals the presence of high AODs previously undetected.The maps were created using ESRI ArcGIS Pro 3.0.1.

Table 1 .
Statistics of continent-stratified cross-validation results of the 4D-STET model in predicting daily PM2.5 levels from 2017 to 2022.

Table 2 .
Top 20 countries with sorted daily exposure risks, showing the percentage of days surpassing the WHO-recommended short-term air quality guideline (S-AQG) level and interim targets (S-IT4 -S-IT1) in 2022.

Table 3 .
Top 20 major cities (defined as urban agglomerations with populations greater than 300,000 reported by the World Urbanization Prospects), showing the percentage of days surpassing the WHO-recommended short-term air quality guideline (S-AQG) level and interim targets (S-IT4 -S-IT1) in 2022.

Table 4 .
Summary of datasets and sources used in this study.